Difference between revisions of "2018 NeuralNaturalLanguageInferenceM"
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- (Chen et al., 2018) ⇒ Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, and Si Wei. (2018). “Neural Natural Language Inference Models Enhanced with External Knowledge.” In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018). doi:10.18653/v1/p18-1224
Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.
|2018 NeuralNaturalLanguageInferenceM||Qian Chen|
|Neural Natural Language Inference Models Enhanced with External Knowledge||10.18653/v1/p18-1224||2018|